Promoting Security and Trust on Social Networks: Explainable Cyberbullying Detection Using Large Language Models in a Stream-Based Machine Learning Framework
This work addresses the need for timely and trustworthy cyberbullying detection to enhance online safety, though it appears incremental by combining existing methods in a new framework.
The authors tackled the problem of detecting cyberbullying on social media by proposing a real-time solution using stream-based machine learning and large language models for feature engineering, achieving performance close to 90% in all evaluation metrics and surpassing competing works.
Social media platforms enable instant and ubiquitous connectivity and are essential to social interaction and communication in our technological society. Apart from its advantages, these platforms have given rise to negative behaviors in the online community, the so-called cyberbullying. Despite the many works involving generative Artificial Intelligence (AI) in the literature lately, there remain opportunities to study its performance apart from zero/few-shot learning strategies. Accordingly, we propose an innovative and real-time solution for cyberbullying detection that leverages stream-based Machine Learning (ML) models able to process the incoming samples incrementally and Large Language Models (LLMS) for feature engineering to address the evolving nature of abusive and hate speech online. An explainability dashboard is provided to promote the system's trustworthiness, reliability, and accountability. Results on experimental data report promising performance close to 90 % in all evaluation metrics and surpassing those obtained by competing works in the literature. Ultimately, our proposal contributes to the safety of online communities by timely detecting abusive behavior to prevent long-lasting harassment and reduce the negative consequences in society.